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An age assessment model for maturity of city brain development maturity
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Science & Technology Review | 2022, 40(14) : 80 - 91
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Science & Technology Review | 2022, 40(14): 80-91
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An age assessment model for maturity of city brain development maturity
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LIU Feng1,2,3, LIU Ying4
Affiliations
    1. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China;
    2. Techxcope Digital Brain Institute, Beijing 100080, China;
    3. Tianfu Institute of International Big Data Strategy and Technology, Chengdu 610218, China;
    4. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
Published: 2022-07-28 doi: 10.3981/j.issn.1000-7857.2022.14.009
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This paper suggests that the city brain is the product of the evolution of the Internet from a mesh to a brain-like architecture since the 21st century and it is combined with the construction of smart cities. Through a comparison with the developmental process of the biological brains, an age assessment model of the maturity of the city brain development is established。 With the evaluation model, the various age groups are first identified through the unified planning scope of the urban brain. The District and the County correspond to 0-5 years old (children level), the City corresponds to 6-11 years old (children level), and the Province corresponds to 12-17 years old (juvenile level) , the Country corresponds to 18-23 year old (youth level), and the world corresponds to 24-29 years old (adult level).Then with the evaluation model, the construction quality is evaluated from six aspects: the problem solving, the digital neuron, the cloud reflex arc, the cost performance, the safety, and the integrity. After the evaluation value is converted into an age increment, it is added to the minimum value of the age group to form the developmental age of the city brain.
city brain  /  smart city  /  development maturity
LIU Feng, LIU Ying. An age assessment model for maturity of city brain development maturity[J]. Science & Technology Review, 2022 , 40 (14) : 80 -91 . DOI: 10.3981/j.issn.1000-7857.2022.14.009
Year 2022 volume 40 Issue 14
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doi: 10.3981/j.issn.1000-7857.2022.14.009
  • Receive Date:2022-06-08
  • Online Date:2022-09-02
  • Published:2022-07-28
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  • Received:2022-06-08
  • Revised:2022-07-20
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https://castjournals.cast.org.cn/joweb/kjdb/EN/10.3981/j.issn.1000-7857.2022.14.009
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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